Bioinformatic Identification and Experimental Validation of a Prognostic Transcriptional Signature Derived from Asparagine Metabolism-Related Genes in Breast Cancer.

Tianyang Liu, Guijuan Zhang, Jialin Li, Xianxin Yan, Min Ma

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Article in Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Tianyang LiuSchool of Traditional Chinese Medicine, State Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, Jinan University, Guangzhou 510632, China.ORCID 0009-0005-6653-6806

Guijuan ZhangSchool of Nursing, Jinan University, Guangzhou 510632, China.

Jialin LiThe Oncology Department, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China.

Xianxin YanSchool of Traditional Chinese Medicine, State Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, Jinan University, Guangzhou 510632, China.

Min MaSchool of Traditional Chinese Medicine, State Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, Jinan University, Guangzhou 510632, China.

Funding

National Natural Science Foundation of China Nos. 82074430 and 81803979the Fourth Batch of TCM Clinical Outstanding Talent Program of China No. 444258the Fundamental Research Funds for the Central Universities No. 21623122the Guangdong Basic and Applied Basic Research Foundation Nos. 2022A1515011674, 2024A1515011722, 2024A1515012176, and 2026A1515012018the Natural Science Foundation of Guangdong Province No. 2018A030313393the Science and Technology Planning Project of Guangzhou Nos. 2024B03J1261 and 2024B03J1262the Science and Technology Project in Guangzhou No. 202102070001the Scientific Research Project of Administration of Traditional Chinese Medicine of Guangdong Province of China No. 20241062the Undergraduate Innovation and Entrepreneurship Training Program of Guangdong Province No. S202410559127

6 · The paper itself

Abstract

Breast cancer (BRCA) possesses prominent molecular heterogeneity, where aberrant expression of genes annotated to asparagine metabolism networks drives malignant progression and therapeutic resistance. However, systematic construction of prognostic signatures from a holistic asparagine metabolic pathway perspective remains scarce, limiting the clinical translation of metabolic insights into prognostic tools. We integrated TCGA and GEO BRCA transcriptomic datasets to screen asparagine metabolism-related differentially expressed genes and build a prognostic model. Six biomarkers, SLC35A2, SRD5A2, NT5E, CEL, IFNG and CNR1, were selected via univariate Cox, LASSO and multivariate Cox regression. SRD5A2 and IFNG were enriched in low-risk patients, while the other four genes were upregulated in high-risk subgroups. This signature reliably stratifies patient prognosis, with risk scores correlating strongly with pathway activity, immune infiltration, immune checkpoints, mutation landscapes and drug responsiveness. Bioinformatic results were validated via TCGA cohort analysis, in vitro cellular assays and Western blot. Two in vivo models were established: 4T1 xenografts in 6-week-old BALB/c mice and DMBA/hormone-induced spontaneous breast tumors in 8-week-old SD rats. Tumors were generated by cell injection or DMBA gavage plus cyclic hormone treatment, and tissue sections were processed for immunohistochemistry. Consistent differential expression of the six core genes was validated across all in vitro and in vivo systems. In conclusion, this asparagine metabolism-associated signature offers candidate biomarkers for personalized prognosis and provides preclinical evidence for metabolism-targeted BRCA therapy.

Indexed as

asparagine metabolism-related genesbreast cancerexperimental validationmachine learningprognostic signature

Identifiers

PMID42651744

DOI10.3390/biology15161441

PMCPMC13509382

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.